SoroushVahidi/scidocs huggingface dataset:
Highway long short-term memory RNNS for distant speech recognition
(2016)

In this paper, we extend the deep long short-term memory (DL-STM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains 43.9/47.7% WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with 15.7% and 5.3% relative improvement respectively.
A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS
(2017)

Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.
Inferring user demographics and social strategies in mobile social networks
(2014)

Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns. Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people.
 We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable prediction-inferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender/age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10% compared with several alternative methods.
An implementation of the earliest deadline first algorithm in Linux
(2009)

Recently, many projects have been started to introduce some real-time mechanisms into general purpose operating systems (GPOS) in order to make them capable of providing the users with some temporal guarantees. Many of these projects focused especially on Linux for its capillary and widespread adoption throughout many different research and industrial environments.
 By tracking the kernel release cycle, we propose an efficient Earliest Deadline First implementation in the form of a patch-set against the 2.6.27 version, that is the latest released one, as of now. Our implementation provides the user with the possibility to choose SCHED_EDF as one of the possible scheduling policies for a task, with an enhanced version of the standard algorithm. In fact, we propose a new approach to shared resources' access which, differently from many other previous existing works, does not require the user to specify any parameters about the critical sections every task will enter during its execution.
Path-space manipulation of physically-based light transport
(2013)

Industry-quality content creation relies on tools for lighting artists to quickly prototype, iterate, and refine final renders. As industry-leading studios quickly adopt physically-based rendering (PBR) across their art generation pipelines, many existing tools have become unsuitable as they address only simple effects without considering underlying PBR concepts and constraints. We present a novel light transport manipulation technique that operates directly on path-space solutions of the rendering equation. We expose intuitive direct and indirect manipulation approaches to edit complex effects such as (multi-refracted) caustics, diffuse and glossy indirect bounces, and direct/indirect shadows. With our sketch- and object-space selection, all built atop a parameterized regular expression engine, artists can search and isolate shading effects to inspect and edit. We classify and filter paths on the fly and visualize the selected transport phenomena. We survey artists who used our tool to manipulate complex phenomena on both static and animated scenes.
What's Your Next Move: User Activity Prediction in Location-based Social Networks
(2013)

Location-based social networks have been gaining increasing popularity in recent years. To increase users’ engagement with location-based services, it is important to provide attractive features, one of which is geo-targeted ads and coupons. To make ads and coupon delivery more effective, it is essential to predict the location that is most likely to be visited by a user at the next step. However, an inherent challenge in location prediction is a huge prediction space, with millions of distinct check-in locations as prediction target. In this paper we exploit the check-in category information to model the underlying user movement pattern. We propose a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predict the most likely location given the estimated category distribution. The advantages of modeling the category level include a significantly reduced prediction space and a precise expression of the semantic meaning of user activities. Extensive experimental results show that, with the predicted category distribution, the number of location candidates for prediction is 5.45 times smaller, while the prediction accuracy is 13.21% higher.
Towards the Semantic Web: Collaborative Tag Suggestions
(2006)

Content organization over the Internet went through several interesting phases of evolution: from structured di rectories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step toward s the semantic web vision. Tagging allows ranking and dat a organization to directly utilize inputs from end us er , enabling machine processing of Web content. Since tags are c reated by individual users in a free form, one important prob lem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set o f general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popu larity to ensure tag quality. We propose a collaborative tag su gestion algorithm using these criteria to spot high-quality ags. The proposed algorithm employs a goodness measure for t ags derived from collective user authorities to combat spam. Th e goodness measure is iteratively adjusted by a reward-penalty lgorithm, which also incorporates other sources of tags, e.g. , content-based auto-generated tags. Our experiments based on My We b 2.0 show that the algorithm is effective.
The Effect of Focal Loss in Semantic Segmentation of High Resolution Aerial Image
(2018)

The semantic segmentation of High Resolution Remote Sensing (HRRS) images is the fundamental research area of the earth observation. Convolutional Neural Network (CNN), which has achieved superior performance in computer vision task, is also useful for semantic segmentation of HRRS images. In this work, focal loss is used instead of cross-entropy loss in training of CNN to handle the imbalance in training data. To evaluate the effect of focal loss, we train SegNet and FCN with focal loss and confirm improvement in accuracy in ISPRS 2D Semantic Labeling Contest dataset, especially when $\gamma$ is 0.5 in SegNet.
Co-Design for Low Warpage and High Reliability in Advanced Package with TSV-Free Interposer (TFI)
(2017)

TSV-Free Interposer (TFI) technology eliminates TSV fabrication and reduces manufacturing and material cost. Co-design modelling methodology is established for TFI technology with considering wafer process, package assembly and package/board level reliability and thermal performance to optimize structure design, wafer process, assembly process and material selection. Experimental results are used for validating warpage modelling results. Through wafer level modelling, suitable carrier wafer and EMC materials are recommended to control wafer warpage less than 2mm. Effects of package substrate coefficient of thermal expansion (CTE) and stiffener on assembly induced package warpage are simulated to reduce package warpage. The recommended materials and geometry design based on reliability are aligned with that from wafer and package warpage simulation results. The final test vehicle (TV) design and material selection are determined based on co-design modelling results for achieving successful TFI wafer process and package assembly process and long term package/board level reliability.
Learning Fine-grained Features via a CNN Tree for Large-scale Classification
(2018)

We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by learning features only among these classes. Such features are expected to be more discriminative, compared to features learned for all the classes. We develop a new algorithm to effectively learn the tree structure among a large number of classes. Experiments on large-scale image classification tasks demonstrate that our method could boost the performance of a given basic CNN model. Our method is quite general, hence it can potentially be used in combination with many other deep learning models.
SIMPLIFIED SOLAR TRACKING PROTOTYPE
(2010)

Solar energy is rapidly advancing as an important means of renewable energy resource. More energy is produced by tracking the solar panel to remain aligned to the sun at a right angle to the rays of light. This paper describes in detail the design and construction of a prototype for solar tracking system with two degrees of freedom, which detects the sunlight using photocells. The control circuit for the solar tracker is based on a PIC16F84A microcontroller (MCU). This is programmed to detect the sunlight through the photocells and then actuate the motor to position the solar panel where it can receive maximum sunlight. Keyword: PIC MCU, tracking, photocell, frame, motion system
Real-time automatic tag recommendation
(2008)

Tags are user-generated labels for entities. Existing research on tag recommendation either focuses on improving its accuracy or on automating the process, while ignoring the efficiency issue. We propose a highly-automated novel framework for real-time tag recommendation. The tagged training documents are treated as triplets of (words, docs, tags), and represented in two bipartite graphs, which are partitioned into clusters by Spectral Recursive Embedding (SRE). Tags in each topical cluster are ranked by our novel ranking algorithm. A two-way Poisson Mixture Model (PMM) is proposed to model the document distribution into mixture components within each cluster and aggregate words into word clusters simultaneously. A new document is classified by the mixture model based on its posterior probabilities so that tags are recommended according to their ranks. Experiments on large-scale tagging datasets of scientific documents (CiteULike) and web pages del.icio.us) indicate that our framework is capable of making tag recommendation efficiently and effectively. The average tagging time for testing a document is around 1 second, with over 88% test documents correctly labeled with the top nine tags we suggested.
Superfamilies of evolved and designed networks.
(2004)

Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents "rate-limited" information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.
Improving Alzheimer's disease diagnosis with machine learning techniques.
(2011)

There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.
Cloud Automation: Precomputing Roadmaps for Flexible Manipulation
(2015)

The goal of this article is to highlight the benefits of cloud automation for industrial adopters and some of the research challenges that must be addressed in this process. The focus is on the use of cloud computing for efficiently planning the motion of new robot manipulators designed for flexible manufacturing floors. In particular, different ways that a robot can interact with a computing cloud are considered, where an architecture that splits computation between the remote cloud and the robot appears advantageous. Given this synergistic robot-cloud architecture, this article describes how solutions from the recent literature can be employed on the cloud during a periodically updated preprocessing phase to efficiently answer manipulation queries on the robot given changes in the workspace. In this setup, interesting tradeoffs arise between path quality and computational efficiency, which are evaluated through simulation. These tradeoffs motivate further research on how motion planning should be executed given access to a computing cloud.
Soft Robot Arm Inspired by the Octopus
(2012)

The octopus is a marine animal whose body has no rigid structures. It has eight arms composed of a peculiar muscular structure, named a muscular hydrostat. The octopus arms provide it with both locomotion and grasping capabilities, thanks to the fact that their stiffness can change over a wide range and can be controlled through combined contractions of the muscles. The muscular hydrostat can better be seen as a modifiable skeleton. Furthermore, the morphology the arms and the mechanical characteristics of their tissues are such that the interaction with the environment (i.e., water) is exploited to simplify control. Thanks to this effective mechanism of embodied intelligence, the octopus can control a very high number of degrees of freedom, with relatively limited computing resources. From these considerations, the octopus emerges as a good model for embodied intelligence and for soft robotics. The prototype of a robot arm has been built based on an artificial muscular hydrostat inspired to the muscular hydrostat of the Octopus vulgaris. The prototype presents the morphology of the biological model and the broad arrangement of longitudinal and transverse muscles. Actuation is obtained with cables (longitudinally) and with shape memory alloy springs (transversally). The robot arm combines contractions and it can show the basic movements of the octopus arm, like elongation, shortening and bending, in water. © Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2012
Power Modeling and Optimization for OLED Displays
(2012)

Emerging organic light-emitting diode (OLED)-based displays obviate external lighting, and consume drastically different power when displaying different colors, due to their emissive nature. This creates a pressing need for OLED display power models for system energy management, optimization as well as energy-efficient GUI design, given the display content or even the graphical-user interface (GUI) code. In this work, we study this opportunity using commercial QVGA OLED displays and user studies. We first present a comprehensive treatment of power modeling of OLED displays, providing models that estimate power consumption based on pixel, image, and code, respectively. These models feature various tradeoffs between computation efficiency and accuracy so that they can be employed in different layers of a mobile system. We validate the proposed models using a commercial QVGA OLED module and a mobile device with a QVGA OLED display. Then, based on the models, we propose techniques that adapt GUIs based on existing mechanisms as well as arbitrarily under usability constraints. Our measurement and user studies show that more than 75 percent display power reduction can be achieved with user acceptance.
The 12-Core POWER8™ Processor With 7.6 Tb/s IO Bandwidth, Integrated Voltage Regulation, and Resonant Clocking
(2015)

POWER8™ is a 12-core processor fabricated in IBM's 22 nm SOI technology with core and cache improvements driven by big data applications, providing 2.5× socket performance over POWER7+™. Core throughput is supported by 7.6 Tb/s of off-chip I/O bandwidth which is provided by three primary interfaces, including two new variants of Elastic Interface as well as embedded PCI Gen-3. Power efficiency is improved with several techniques. An on-chip controller based on an embedded PowerPC™ 405 processor applies per-core DVFS by adjusting DPLLs and fully integrated voltage regulators. Each voltage regulator is a highly distributed system of digitally controlled microregulators, which achieves a peak power efficiency of 90.5%. A wide frequency range resonant clock design is used in 13 clock meshes and demonstrates a minimum power savings of 4%. Power and delay efficiency is achieved through the use of pulsed-clock latches, which require statistical validation to ensure robust yield.
Visceral pain: the ins and outs, the ups and downs.
(2012)

PURPOSE OF REVIEW
Visceral pain represents a major clinical problem, yet far less is known about its mechanisms compared with somatic pains, for example, from cutaneous and muscular structures.


RECENT FINDINGS
In this review, we describe the neuroanatomical bases of visceral pain signalling in the peripheral and central nervous system, comparing to somatic pains and also the channels and receptors involved in these events. We include an overview of potential new targets in the context of mechanisms of visceral pain and hypersensitivity.


SUMMARY
This review should inform on the recognition of what occurs in patients with visceral pain, why comorbidities are common and how analgesic treatments work.
On the Use of Logic Trees for Ground-Motion Prediction Equations in Seismic-Hazard Analysis
(2005)

Logic trees are widely used in probabilistic seismic hazard analysis as a tool to capture the epistemic uncertainty associated with the seismogenic sources and the ground-motion prediction models used in estimating the hazard. Combining two or more ground-motion relations within a logic tree will generally require several conversions to be made, because there are several definitions available for both the predicted ground-motion parameters and the explanatory parameters within the predictive ground-motion relations. Procedures for making conversions for each of these factors are presented, using a suite of predictive equations in current use for illustration. The sensitivity of the resulting ground-motion models to these conversions is shown to be pronounced for some of the parameters, especially the measure of source-to-site distance, highlighting the need to take into account any incompatibilities among the selected equations. Procedures are also presented for assigning weights to the branches in the ground-motion section of the logic tree in a transparent fashion, considering both intrinsic merits of the individual equations and their degree of applicability to the particular application.
Privacy-Preserving Data Mining
(2000)

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.